A region linear model is widely used for an actual device as a model representation with high interpretability. In such a model, an input space is divided into several subspaces (hereinafter, also described as regions), and then a simple model is set for each region. In this way, the model with a structure using regions has excellent prediction performance even for non-linearity due to a plurality of partitions, and interpretation of data is also easy.
As a model dividing the region, a decision tree model and a model described in NPL 1 have been known. NPL 1 describes a method for adaptively dividing a feature space into different multiple regions and learning a prediction model of each region. Each model optimizes an objective function for the prediction model in region division and each divided region.